Spatial regression models over two-dimensional manifolds

By B. Ettinger, Simona Perotto, Laura M. Sangalli. 2016

51 citations recorded by the source corpus.

Subfields: Environmental Engineering, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c64123e01dfe7096bfdffe670d11ee1084250d4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Fabian Scheipl
2. Sonja Greven

In Statistics and Probability

1. Sonja Greven
2. Fabian Scheipl
3. Simon N. Wood
4. C. Hassall

Citations in the corpus

• A general framework for functional regression modelling (2017, background): “…more complex, regression models are required for more general objects, for example, trajectories in 2D or 3D space, images (e.g., Goldsmith et al., 2014) or functions on manifolds (e.g., Ettinger et al., 2016), shapes (e.g., Dryden, 2014) or even objects such as trees (e.g., Wang and Marron, 2007).”
• A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province (2016, unknown): “Finally, data distributed over curved domains, instead of over planar domains, could be handled by extending the model proposed in Ettinger et al (2016).”